**1. Introduction**

This chapter envisions the possibility of continuous risk assessment with non-invasive monitoring using tools of Medical Cybernetics (MC) facilitating early diagnosis and prevention of type 2 diabetes (DM2) and its complications including cardiovascular disease (CVD), chronic kidney disease (CKD) and heart failure (HF). MC offers a suitable conceptual framework to make the pathological processes of DM2, CVD, CKD, and HF observable and controllable through

appropriate interventions facilitated by mathematical modeling. Utilizing principles of MC has the potential to enable primary care to help more beyond current standard of care and to make Digital Health more accessible to our patients. Moving away from traditional reductionism and embracing holistic approaches will certainly help fulfill the promise of Medical Cybernetics (MC) and help find workable solutions to tackle the ever growing health related challenges of humanity and introduce new approaches to manage and self-manage chronic non-communicative conditions or diseases in the 21st century. Already available information technologies like smart phones, cloud computing and the widely available sensor devices of the fitness industry could be put together into a cyber-physical system (CPS) to gain needed data and tools and to provide a holistic approach. The principle idea behind using MC [1–9] and developing a CPS [5, 6] is to gain deep insight and make so far unmeasurable phenomena indirectly calculable in the users' natural environment and put these unknown phenomena in the appropriate context for improved control. The plethora of new data gained with such CPS will lead to the creation of needed metrics and open opportunities for optimized self-control and dynamic behavior interventions based on the targeted metrics, leading to self-healing and cyber-therapy supervised by health care providers.

Ori Diagnostic Instruments, LLC (ODI) has been conducting R&D [1–11] and recently we introduced a CPS [5, 6]. CPS is a mobile technology integrating sensory data from various mobile devices into individualized dynamic mathematical models of physiological processes allowing for analysis and prediction using the models and allowing for quasi-real time feedback to the user (and optionally the primary provider). We have developed several technical and medical innovations allowing for creation of a CPS: 1. Self-adaptive models of the human energy metabolism (SAM-HEM) [1–11]; 2. Self-improving measurement models to amend validity, reliability, consistency, and accuracy of bioelectrical measurements [7–9]; 3. Using the minimum variance Kalman filter along with state space modeling technique [1–11] where process models of state variables work in unison with measurement models, mutually updating each other's *a priori* and *a posteriori* model calculations with the help of the minimum variance Kalman filter; 4. Utilizing principles of "least action/ stationary action" to obtain essential practically unmeasurable parameters of the human energy metabolism [5–6]; 5. Applying principles of "maximum information entropy" to evaluate stochastic processes and perform parameter estimations with constraints or subsidiary conditions [7–10]; 6. Feasibility demonstration of our process modeling technologies in simulation studies using published trial data [1–6, 11]; 7. Innovations using a CPS to reenergize primary care and facilitate goals of Global Health [4, 10, 11].

Some important advantages of ODI's innovations to combat noncommunicable cardiometabolic diseases are the following. 1. An important aspect of ODI's innovations is the integration of self-adapting models into a cloud based cyber-physical system [5, 6] that provides user feedback and allows for truly individualized patient-oriented approaches. 2. Further it is anticipated that ODI's holistic and data driven individualized diagnostic approach will allow not just to help prevention and improve management and self-management of chronic conditions related to DM2 but also to lend help during emerging medical emergencies [7, 10]. 3. It is envisioned here that as more and more wearable physiological sensors become available, the sensors can be integrated with our cyber-physical system platform and their respective self-adaptive pathophysiological process models and self-learning measurement models [10, 11]. 4. A user's individual dynamic mathematical models provide feedback and prediction to assist behavior modification by supporting and maximizing control [10, 11]. 5. A CPS can realize not just a complex adaptive system at the individual level, but also through interconnections a network

**409**

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

of individualized cyber-physical systems can be realized, allowing for network analysis and machine learning/artificial intelligence. Global Health goals [10, 11]

This chapter consists of two parts. In part I, we will show how already widely available information technologies like smart phones, cloud computing, and sensor devices of the fitness industry could be put together into an integrated cyberphysical system (ICPS) to support fitness goals like fighting increased insulin resistance and low level of cardiorespiratory fitness and help in building resilience with improved physiological reserve capacity. This form of ICPS supports fitness goals without the wider scope of a "medical software "i.e. without the intention of medical treatment. This non-medical software ICPS focuses on three interlinked physiological/pathophysiological processes: 1. Cardiometabolic Functioning and Disease (CMD), 2. Cardiovascular Functioning and Disease (CVD), and 3. Cardiovegetative Functioning and Stress (CVS). We will show how representative metrics reflecting health in these areas of physiological functioning and early disease can be created using MC modeling using data from a wearable sensor system (SS). Regarding CMD, the reader will be informed about how the non-invasive measurement of insulin resistance is possible with the R- or Rw-ratio which follows changes of the invasively measured HOMA-IR. R- or Rw-ratio related estimates are derived from serially measured weight, fat weight by bioimpedance measurement, and energy balance related data [3–6]. The CVD health status is assessed by indirectly estimating maximal oxygen uptake (VO2max) from daily physical activity and heart rate related data [5]. Indices of CVS health are obtained from time and

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

could be approached at a community or even societal level.

frequency domain analysis of heart rate variability (HRV) [12, 13].

In part II an upgraded version of ICPS to medical software will be outlined which still has to be developed and clinically tried and properly examined and verified according to applicable rules and regulations by FDA. The major reason for distinction between non-medical software and FDA approved medical software is that the former primarily serves the purpose of prevention of prediabetes, DM2 and complications as opposed to the latter where medical diagnosis is made requiring active therapeutic interventions by health professionals. The part II subchapter is inspired also by the most recent summary recommendation for person-centered, outcomes-driven treatments of DM2 in primary care by leading academic authors [14]. One of the key points of this article is to call for "a patient-centered approach that addresses patients' multimorbidities, needs, preferences, and barriers and includes diabetes education and lifestyle interventions as well as pharmacologic treatment…". The medical software version of ICPS could be complementary to key points in [14]. We introduce here to the reader how the following comorbid conditions could be observed non-invasively and how metrics can be created to see outcomes objectively. We will discuss here the following pathological processes as targets of monitoring, tracking and metric creation for outcome measures: 1. CMD and Metabolic Syndrome (MS), 2. Atherosclerotic Cardiovascular Disease (ASCD), 3. Autonomic Dysfunction (AD), 4. Chronic Anemia due to CKD, and 5. Heart failure (HF). Here we want to point out that [14] puts great emphasis on ASCD, CKD and HF as a targeted outcome measure for interventions. It is envisioned here that self-explaining metrics regarding disease processes 1–5 can be displayed quasi real time on the patient's smart phone app giving tremendous opportunity for patients to educate themselves and learn more about their diseases and ask appropriate questions. The feedback of information may help improve self-management in a non-judgmental manner. The self-explaining nature of metrics may also point out individual responsibilities to fight modifiable risk factors. Having quantifiable metrics allows for dynamic lifestyle interventions which could be managed, selfmanaged or helped with automated feedback of information. Further, the response

#### *Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*

of individualized cyber-physical systems can be realized, allowing for network analysis and machine learning/artificial intelligence. Global Health goals [10, 11] could be approached at a community or even societal level.

This chapter consists of two parts. In part I, we will show how already widely available information technologies like smart phones, cloud computing, and sensor devices of the fitness industry could be put together into an integrated cyberphysical system (ICPS) to support fitness goals like fighting increased insulin resistance and low level of cardiorespiratory fitness and help in building resilience with improved physiological reserve capacity. This form of ICPS supports fitness goals without the wider scope of a "medical software "i.e. without the intention of medical treatment. This non-medical software ICPS focuses on three interlinked physiological/pathophysiological processes: 1. Cardiometabolic Functioning and Disease (CMD), 2. Cardiovascular Functioning and Disease (CVD), and 3. Cardiovegetative Functioning and Stress (CVS). We will show how representative metrics reflecting health in these areas of physiological functioning and early disease can be created using MC modeling using data from a wearable sensor system (SS). Regarding CMD, the reader will be informed about how the non-invasive measurement of insulin resistance is possible with the R- or Rw-ratio which follows changes of the invasively measured HOMA-IR. R- or Rw-ratio related estimates are derived from serially measured weight, fat weight by bioimpedance measurement, and energy balance related data [3–6]. The CVD health status is assessed by indirectly estimating maximal oxygen uptake (VO2max) from daily physical activity and heart rate related data [5]. Indices of CVS health are obtained from time and frequency domain analysis of heart rate variability (HRV) [12, 13].

In part II an upgraded version of ICPS to medical software will be outlined which still has to be developed and clinically tried and properly examined and verified according to applicable rules and regulations by FDA. The major reason for distinction between non-medical software and FDA approved medical software is that the former primarily serves the purpose of prevention of prediabetes, DM2 and complications as opposed to the latter where medical diagnosis is made requiring active therapeutic interventions by health professionals. The part II subchapter is inspired also by the most recent summary recommendation for person-centered, outcomes-driven treatments of DM2 in primary care by leading academic authors [14]. One of the key points of this article is to call for "a patient-centered approach that addresses patients' multimorbidities, needs, preferences, and barriers and includes diabetes education and lifestyle interventions as well as pharmacologic treatment…". The medical software version of ICPS could be complementary to key points in [14]. We introduce here to the reader how the following comorbid conditions could be observed non-invasively and how metrics can be created to see outcomes objectively. We will discuss here the following pathological processes as targets of monitoring, tracking and metric creation for outcome measures: 1. CMD and Metabolic Syndrome (MS), 2. Atherosclerotic Cardiovascular Disease (ASCD), 3. Autonomic Dysfunction (AD), 4. Chronic Anemia due to CKD, and 5. Heart failure (HF). Here we want to point out that [14] puts great emphasis on ASCD, CKD and HF as a targeted outcome measure for interventions. It is envisioned here that self-explaining metrics regarding disease processes 1–5 can be displayed quasi real time on the patient's smart phone app giving tremendous opportunity for patients to educate themselves and learn more about their diseases and ask appropriate questions. The feedback of information may help improve self-management in a non-judgmental manner. The self-explaining nature of metrics may also point out individual responsibilities to fight modifiable risk factors. Having quantifiable metrics allows for dynamic lifestyle interventions which could be managed, selfmanaged or helped with automated feedback of information. Further, the response

*Type 2 Diabetes - From Pathophysiology to Cyber Systems*

cyber-therapy supervised by health care providers.

appropriate interventions facilitated by mathematical modeling. Utilizing principles of MC has the potential to enable primary care to help more beyond current standard of care and to make Digital Health more accessible to our patients. Moving away from traditional reductionism and embracing holistic approaches will certainly help fulfill the promise of Medical Cybernetics (MC) and help find workable solutions to tackle the ever growing health related challenges of humanity and introduce new approaches to manage and self-manage chronic non-communicative conditions or diseases in the 21st century. Already available information technologies like smart phones, cloud computing and the widely available sensor devices of the fitness industry could be put together into a cyber-physical system (CPS) to gain needed data and tools and to provide a holistic approach. The principle idea behind using MC [1–9] and developing a CPS [5, 6] is to gain deep insight and make so far unmeasurable phenomena indirectly calculable in the users' natural environment and put these unknown phenomena in the appropriate context for improved control. The plethora of new data gained with such CPS will lead to the creation of needed metrics and open opportunities for optimized self-control and dynamic behavior interventions based on the targeted metrics, leading to self-healing and

Ori Diagnostic Instruments, LLC (ODI) has been conducting R&D [1–11] and recently we introduced a CPS [5, 6]. CPS is a mobile technology integrating sensory data from various mobile devices into individualized dynamic mathematical models of physiological processes allowing for analysis and prediction using the models and allowing for quasi-real time feedback to the user (and optionally the primary provider). We have developed several technical and medical innovations allowing for creation of a CPS: 1. Self-adaptive models of the human energy metabolism (SAM-HEM) [1–11]; 2. Self-improving measurement models to amend validity, reliability, consistency, and accuracy of bioelectrical measurements [7–9]; 3. Using the minimum variance Kalman filter along with state space modeling technique [1–11] where process models of state variables work in unison with measurement models, mutually updating each other's *a priori* and *a posteriori* model calculations with the help of the minimum variance Kalman filter; 4. Utilizing principles of "least action/ stationary action" to obtain essential practically unmeasurable parameters of the human energy metabolism [5–6]; 5. Applying principles of "maximum information entropy" to evaluate stochastic processes and perform parameter estimations with constraints or subsidiary conditions [7–10]; 6. Feasibility demonstration of our process modeling technologies in simulation studies using published trial data [1–6, 11]; 7. Innovations using a CPS to reenergize primary care and facilitate goals

Some important advantages of ODI's innovations to combat noncommunicable cardiometabolic diseases are the following. 1. An important aspect of ODI's innovations is the integration of self-adapting models into a cloud based cyber-physical system [5, 6] that provides user feedback and allows for truly individualized patient-oriented approaches. 2. Further it is anticipated that ODI's holistic and data driven individualized diagnostic approach will allow not just to help prevention and improve management and self-management of chronic conditions related to DM2 but also to lend help during emerging medical emergencies [7, 10]. 3. It is envisioned here that as more and more wearable physiological sensors become available, the sensors can be integrated with our cyber-physical system platform and their respective self-adaptive pathophysiological process models and self-learning measurement models [10, 11]. 4. A user's individual dynamic mathematical models provide feedback and prediction to assist behavior modification by supporting and maximizing control [10, 11]. 5. A CPS can realize not just a complex adaptive system at the individual level, but also through interconnections a network

**408**

of Global Health [4, 10, 11].

to pharmacological interventions could be gauged, helping to track results of treatment and recognize inadvertent side effects.

To our knowledge there is no noninvasive tool or monitoring device available to measure increased oxidative stress, inflammation, or insulin resistance in the user's natural environment. However, these pathological processes are strongly interlinked, leading to among others DM2, CMD, MS, CVD, ASCD, AD, Chronic Anemia of CKD, and HF. Importantly, ICPS is built on the holistic modeling approach of considering the entire human energy metabolism and insulin resistance. The latter can be viewed also as a surrogate marker for whole body oxidative stress and inflammation [15]. The bio-physical principle behind the proposed conceptual framework of ICPS and for process models is the recognition that the changes of the body composition (lean mass and fat mass) and the energy flow in and out of the body are governed by the fat vs. carbohydrate burning ratio and are strongly linked to insulin resistance [16, 17]. The significance of this is that an impaired mitochondrial lipid oxidation is a major anomaly in the chain of metabolic events leading to obesity and increase of insulin resistance [18]. High insulin resistance is associated with high respiratory quotient (RQ ) reflecting lower fat burning than normal [19]. We have no non-invasive measuring technique for Oxidative Stress. However, there is a strong connection between Oxidative Stress and Insulin Resistance [20]. Similarly, there are strong connections between inflammation and insulin resistance [21] but there is no non-invasive tool available currently to monitor whole body inflammation. Therefore, we intend to use the R- and Rw ratio to give at least a qualitative signal tool if the trends of changes in the metabolism are in the right or wrong direction in terms oxidative stress and inflammation. *Our central hypothesis is that by improving insulin resistance with the use of ICPS, we can ameliorate the condition of oxidative stress, overall inflammation, fat vs. carbohydrate oxidation, and cardiovascular disease progression***.**

To our knowledge ODI is the first in using the principle of "least action/ stationary action" as a principle for finding key physiological parameters of the energy metabolism [5, 6]. This is instrumental to estimate noninvasively the HOMA- IR linked marker of insulin resistance R- or Rw-ratio which are defined as R = ΔL/ΔF and Rw = ΔW/ΔF where ΔL, ΔW and ΔF are lean mass, weight and fat mass change over 24 hrs. For monitoring of insulin resistance, we were able to prove the feasibility of this concept [5–6]. Further, we have shown that our Weight, Fat weight, Energy Balance (WFE) model can estimate changes of Rw without mandatory calorie counting by serially measuring weight, fat weight, and energy balance [6]. Our extended model of WFE calculation is called WFE-DNL-AT [6] and allows also for estimating for the first time noninvasively in the user's natural environment the otherwise difficult or impossible to measure changes of state variables (SV's) of the metabolism such as 24 h nonprotein respiratory quotient (24hRQ ), utilized macronutrient energy intake, fat vs. carbohydrate oxidation rate (Fox/Cox), de novo lipogenesis (DNL), and adaptive thermogenesis (AT). However, WFE-DNL-AT calculations require knowledge of the daily macronutrient calorie intake.

For measuring daily changes of fat mass F, lean body mass L, the measurement of intracellular water mass (ICW) as well as extracellular water mass (ECW) are also needed. Unfortunately, bioimpedance measurement technologies are not suitable for clinical use in current form due to significant interindividual variations mainly due to lack of reliable bio-electrical modeling of electrical properties of a body segment. On the other hand, bioimpedance measurements are quite well suited for individualized measurements or serial measurement as the intraindividual variation is small. The electrical modeling issue can be improved with using the principle of "maximum information entropy" [9, 10]. Therefore, ODI developed

**411**

thermogenesis AT is possible.

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes…*

a Body Composition and Hydration Status Analyzer stand up scale (BC-HS-A) [7–10]. We use here several innovations for creating individualized bioimpedance

A general principle of the development of ICPS as medical software is that we want to connect the calculated SV's to morbidity and mortality risks. An example is given in [22] where cumulative incidence of various CVD events is compared in people with and without diabetes. The hazard ratio for CVD in view of HOMA-IR is published in [23]. CVD mortality and all-cause mortality is investigated with low cardiorespiratory fitness according to weight categories in [24]. Waist circumference is connected to mortality in [25]. Mortality is evaluated according to weight status with incidence of diabetes in [26]. CVD and mortality as a function of BMI is published in [27]. Heart Rate Variability and Risk of All-Cause Death and Cardiovascular Events are investigated in [28]. All these published morbidity/mortality studies allow us to assess the time trajectory of likelihood of morbidity and

ICPS generates SV's and metrics in each domain of use (1–5) and displays the results quasi real time on the screen of a mobile app, the Metabolic Health Monitoring (MHM) Mobile app or on the Metabolic Manager Software Tool (MST) Web app. MHM is designed for displaying the SV's quasi real time and for entering input data and providing feedback that is either machine generated or from MST by personal trainer or primary provider. MST is a web app designed for use by personal trainer/primary provider (s) or the user himself/herself for analysis and prediction of the calculated SV's and metrics. MST enables also planning for lifestyle change

Here we introduce ICPS ORI FIT-MET™ for the purpose to achieve fitness and prevent prediabetes, DM2 and complications such as CVD and AD. Uniquely, ICPS can construct trajectories of SV's (metrics) quasi real time in three domains of health: 1. Cardiometabolic Functioning and Disease (CMD), 2. Cardiovascular Functioning and Disease (CVD), and 3. Cardio-vegetative Functioning and Stress (CVS) with major implications to morbidity/mortality risks. Each of these domains have their mathematical process models to estimate the SV's (metrics). ICPS uses the predictive Kalman filter to predict future changes based on serially measured

Ad 1. For CMD we use our Cardiometabolic Function Model (CMFM) which utilizes our Self-Adaptive Model of the Human Energy Metabolism (SAM-HEM) [1–4]; the Weight, Fat weight, Energy Balance model calculation (WFE); and the de novo lipogenesis, adaptive thermogenesis, and 24 hr. respiratory quotient model calculation WFE-DNL-AT [6]. The metric for insulin resistance in terms of R- or Rw-ratio carries the power of allowing to estimate the fat vs. carbohydrate burning and it is reflective of overall oxidative stress and inflammation. The CMFM modeling can calculate and predict the following physiological SV's: weight, fat mass, lean mass, ECW, ICW, R-ratio, Rw-ratio, Fat vs. Carbohydrate Oxidation, and 24 h non-protein respiratory quotient. With precise calorie counting the estimations of utilized macronutrient energy intake, de novo lipogenesis DNL and adaptive

Ad 2. For CVD process modeling ODI uses a cardiovascular fitness model (CVFM) in which the maximum oxygen uptake capacity (VO2max) is estimated

*DOI: http://dx.doi.org/10.5772/intechopen.94232*

mortality as a function of the individually calculated SV's.

**2. ICPS non-medical software (ORI FIT-MET™)**

input data and using the respective predictive model calculation.

and evaluating progress and outcome.

**2.1 Description of the process models**

measurement models [7–10].

*Integrated Cyber-Physical System to Support Early Diagnosis and Prevention of Prediabetes… DOI: http://dx.doi.org/10.5772/intechopen.94232*

a Body Composition and Hydration Status Analyzer stand up scale (BC-HS-A) [7–10]. We use here several innovations for creating individualized bioimpedance measurement models [7–10].

A general principle of the development of ICPS as medical software is that we want to connect the calculated SV's to morbidity and mortality risks. An example is given in [22] where cumulative incidence of various CVD events is compared in people with and without diabetes. The hazard ratio for CVD in view of HOMA-IR is published in [23]. CVD mortality and all-cause mortality is investigated with low cardiorespiratory fitness according to weight categories in [24]. Waist circumference is connected to mortality in [25]. Mortality is evaluated according to weight status with incidence of diabetes in [26]. CVD and mortality as a function of BMI is published in [27]. Heart Rate Variability and Risk of All-Cause Death and Cardiovascular Events are investigated in [28]. All these published morbidity/mortality studies allow us to assess the time trajectory of likelihood of morbidity and mortality as a function of the individually calculated SV's.

ICPS generates SV's and metrics in each domain of use (1–5) and displays the results quasi real time on the screen of a mobile app, the Metabolic Health Monitoring (MHM) Mobile app or on the Metabolic Manager Software Tool (MST) Web app. MHM is designed for displaying the SV's quasi real time and for entering input data and providing feedback that is either machine generated or from MST by personal trainer or primary provider. MST is a web app designed for use by personal trainer/primary provider (s) or the user himself/herself for analysis and prediction of the calculated SV's and metrics. MST enables also planning for lifestyle change and evaluating progress and outcome.
